For centuries the global food and agriculture industry has tolerated significant post-harvest waste, presently estimated at approximately $1 trillion annually. World population is expected to reach 10.5 billion by 2050, further exacerbating global food availability and security concerns. According to research data, food supplies would need to increase by 60% (estimated at 2005 food production levels) in order to meet the food demand by 2050. Food availability and accessibility can vastly improve by increasing production, enhancing logistics, and reducing waste. Thus, reduction of post-harvest waste is a critical component of ensuring future global food security.
Additionally, health and safety concerns feature high on the agendas of governments and regulators. In this respect, the application of agrichemicals needs to be monitored with advanced technology, to ensure proper use (adherence to precise pest control protocols), environmental safety (managing stored product pests and controlling tolerance and resistance to pesticides) and operator safety (controlling leaks of harmful toxic substances).
End users in this field, such as farmers, operators of storage and logistics facilities, agronomists, food scientists, pest control technicians and quality control experts, have used certain methods in the prior art to combat post-harvest waste and its root causes including pest infestations. These methods have employed technology conceived several decades ago, to perform functions such as fumigation chemical (i.e. fumigant) dosage monitoring, insect infestation detection, spoilage detection. Our assessment revealed that none of these legacy solutions and methods in the prior art effectively addressed the trillion-dollar waste problems. Moreover, prior art methods for post-harvest monitoring and quality control have also been manual, error-prone, cumbersome, not scalable, outdated and impractical. They have also been costly without resultant benefits. Specifically:                Draeger-type fumigant meters have the form of tubes. These require a lot of manual effort by experienced operators. There are hazards related to their use. Data is typically recorded by pen and paper.        Certain electronic equipment vendors are offering measurement devices for fumigation monitoring. These require special plumbing for sampling fumigant levels inside storage areas, are difficult to operate and are usually practical only for infrequent sampling. The data collected is not practically correlated with proper fumigation protocols or insect mortality statistics.        Certain crop silo monitoring solutions focus specifically on temperature tracking to detect spoilage as it occurs. They employ old technology (wired thermocouples) and are difficult to install. They also malfunction or get completely destroyed in the presence of fumigant gas. Consequently, when a spoilage hotspot is detected, it is usually already too late to take corrective action.        Insect collectors and detectors such as pheromone traps require manual inspection. Electronic products for insect detection that can transmit insect population data cannot be placed inside bulk product where insects may take refuge. The data collected is not correlated with pest management parameters (such as recent fumigation dosages and durations) or environmental conditions (such as temperature, humidity).        Certain methods in the prior art provide computational means for assessing properties related to the spoilage of crops (such as grains) in storage, by estimating key parameters such as product moisture based on parameters that can be readily observed (such as product temperature and ambient relative humidity). However, these tools are of limited accuracy and usefulness as storage microclimate may change unpredictably (e.g. during a hot and humid weather spell) and thus render any initial estimates invalid. Moreover, the initial conditions of a stored product may not be fully known—e.g. unknowingly mixing a quantity of damp and infested grain with a larger quantity of drier and good quality grain may spread the spoilage and infestation to the entire lot of grain.        Researchers in this field have resorted to numerical analysis techniques such as Computational Fluid Dynamics (CFD) simulations to predict the effects of climate conditions on stored commodities. However, these visionary approaches have not materialized into convenient and handy tools for the actual end user (who is typically not expert in numerical analysis) as they have been overly complicated, not easy to control and re-use and not generic enough to address a good variety of commodity storage scenarios. Besides, these techniques have fallen short of correlating concurrently updated physical parameters with biological effects related to grain spoilage and quality degradation.        